Background: The objective of this study was to predict the mortality
risk of patients during or shortly after cardiac surgery by using
machine learning techniques and their learning abilities from
collected data.

Methods: The dataset was obtained from Acıbadem Maslak
Hospital. Risk factors of the European System for Cardiac Operative
Risk Evaluation (EuroSCORE) were used to predict mortality risk.
First, Standard EuroSCORE scores of patients were calculated and
risk groups were determined, because 30-day follow-up information
of patients was not available in the dataset. Models were created with
five different machine learning algorithms and two different datasets
including age, serum creatinine, left ventricular dysfunction, and
pulmonary hypertension were numeric in Dataset 1 and categorical
in Dataset 2. Model performance evaluation was performed with
10-fold cross-validation.

Results: Data analysis and performance evaluation were performed
with R, RStudio and Shiny. C4.5 was selected as the best algorithm
for risk prediction (accuracy= 0.989) in Dataset 1. This model
indicated that pulmonary hypertension, recent myocardial infarct,
surgery on thoracic aorta are the primary three risk factors
that affect the mortality risk of patients during or shortly after
cardiac surgery. Also, this model is used to develop a dynamic
web application which is also accessible from mobile devices
(https://elifkartal.shinyapps.io/euSCR/).

Conclusion: The C4.5 decision tree model was identified as having
the highest performance in Dataset 1 in predicting the mortality
risk of patients. Using the numerical values of the risk factors
can be useful in increasing the performance of machine learning
models. Development of hospital-specific local assessment systems
using hospital data, such as the application in this study, would be
beneficial for both patients and doctors.

Turkish Journal of Thoracic and Cardiovascular Surgery published orginal papers on topics in cardiovascular surgery, cardiovascular anesthesia,cardiology and thoracic surgery. These encompass all relevant clinical, surgical and laboratory specialities, editorials, current and collective reviews, tecnical knowhow papers, case reports, "How to Do It" papers.
All copyrights of the articles that published or will be published belongs to Turkish Journal of Thoracic and Cardiovascular Surgery and without permission of editorial board whole articles or any part of articles table pictures and graphics could not be published.
Turkish Journal of Thoracic and Cardiovascular Surgery is indexed by Science Citation Index - Expanded (SCIE)